Triple
T20323322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Battle of Annual |
E492263
|
entity |
| Predicate | RifianCasualties |
P6773
|
FINISHED |
| Object | several hundred killed and wounded |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: several hundred killed and wounded | Statement: [Battle of Annual, RifianCasualties, several hundred killed and wounded]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: RifianCasualties Context triple: [Battle of Annual, RifianCasualties, several hundred killed and wounded]
-
A.
nativeCasualties
Indicates that native or indigenous people suffered deaths or injuries as a result of a particular event, action, or conflict.
-
B.
battleCasualty
Indicates that an entity was killed, wounded, or otherwise harmed as a direct result of a specific battle or armed conflict.
-
C.
militaryCasualtiesSide
Indicates the side or party in a conflict to which the recorded military casualties belong.
-
D.
militaryCasualtiesEstimate
chosen
Indicates an estimated number of people killed, wounded, or missing as a result of military conflict or operations.
-
E.
militaryDeaths
Indicates the number of individuals who died while serving in a military capacity, typically during armed conflict or related operations.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e0b4a0134081909113563e1c3ba68a |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6778d95dc81909b1c87d26b5d3a33 |
completed | April 20, 2026, 6:59 p.m. |
| PD | Predicate disambiguation | batch_69e5762655ac8190a8cc48a29fa2c0c4 |
completed | April 20, 2026, 12:41 a.m. |
Created at: April 16, 2026, 11:21 a.m.